Lan Dai

907 citations
39 papers · 701 indexed · h-index 16

Impact in

Papers in

    • Sphingolipid Metabolism and Signaling 10
    • Retinoids in leukemia and cellular processes 6
    • Protein Kinase Regulation and GTPase Signaling 3
    • Reproductive System and Pregnancy 4

Lan Dai

37 papers receiving 694 citations

Peers

Lan Dai
Comparison fields: 5 of 76
  • Cancer Research 233
  • Reproductive Medicine 112
  • Obstetrics and Gynecology 68
  • Immunology 150
  • Molecular Biology 404
Replace R E Leake with:
R E Leake United Kingdom
Shengsheng Yang China
Sufang Wu China
Han‐Hsuan Fu United States
Manon de Ladurantaye Canada
Mary Peavey United States
Lilach Kleinberg Norway
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Ruo-Pan Huang United States
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Citations per field
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Citations per year

Countries citing papers authored by Lan Dai

Since Specialization
Citations

This map shows the geographic impact of Lan Dai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lan Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lan Dai more than expected).

Fields of papers citing papers by Lan Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lan Dai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lan Dai. The network helps show where Lan Dai may publish in the future.

Co-authors

The 25 scholars most cited alongside Lan Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lan Dai Line = papers co-authored together Lan Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201188
2 201485
3 200946
4 201740
5
MiR-199a inhibits the angiogenic potential of endometrial stromal cells under hypoxia by targeting HIF-1α/VEGF pathway.
201538
6 202035
7 201935
8 202031
9 201428
10 201328
11 201328
12 201527
13 201326
14 201325
15 201921
16 201619
17 201915
18 202314
19 202111
20 202110

About Lan Dai

Lan Dai is a scholar working on Molecular Biology, Immunology, Cell Biology, Hematology and Reproductive Medicine, having authored 39 papers that have together received 701 indexed citations. Recurring topics across this work include Sphingolipid Metabolism and Signaling (10 papers), Retinoids in leukemia and cellular processes (6 papers), Caveolin-1 and cellular processes (5 papers), Endometriosis Research and Treatment (4 papers), Reproductive System and Pregnancy (4 papers), Endoplasmic Reticulum Stress and Disease (3 papers), Acute Myeloid Leukemia Research (3 papers) and Protein Kinase Regulation and GTPase Signaling (3 papers). The work is most often cited by research in Cancer Research (233 citations), Reproductive Medicine (112 citations), Obstetrics and Gynecology (68 citations), Immunology (150 citations) and Molecular Biology (404 citations). Lan Dai has collaborated with scholars based in China, Australia and Fiji. Frequent co-authors include Wen Di, Lihua Qiu, Liankun Gu, Keqi Song, Pu Xia, Xingchen Zhou, Yanfei Qi, Liying Gu, Yuan Hu and Yunfei Wang. Their work appears in journals such as Journal of Ovarian Research, PLoS ONE, International Journal of Radiation Oncology*Biology*Physics, Scientific Reports and Molecular Human Reproduction.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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